Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-16T15:20:38.576Z Has data issue: false hasContentIssue false

Determining Exposure to Auxin-Like Herbicides. II. Practical Application to Quantify Volatility

Published online by Cambridge University Press:  20 January 2017

Audie S. Sciumbato*
Affiliation:
Texas Agricultural Experiment Station, Department of Soil and Crop Sciences, College Station, TX 77843-2474
James M. Chandler
Affiliation:
Texas Agricultural Experiment Station, Department of Soil and Crop Sciences, College Station, TX 77843-2474
Scott A. Senseman
Affiliation:
Texas Agricultural Experiment Station, Department of Soil and Crop Sciences, College Station, TX 77843-2474
Rodney W. Bovey
Affiliation:
Texas Agricultural Experiment Station, Department of Rangeland Ecology and Management, College Station, TX 77843-2126
Ken L. Smith
Affiliation:
University of Arkansas– Monticello, Monticello, AR 71656
*
Corresponding author's E-mail: [email protected]

Abstract

Volatility and drift are problems commonly associated with auxin-like herbicides. Field and greenhouse studies were conducted at Texas A & M University to develop a method of quantifying volatility and subsequent off-target movement of 2,4-D, dicamba, and triclopyr. Rate–response curves were established by applying reduced rates ranging from 4 × 10−1 to 1 × 10−5 times the normal use rates of the herbicides to cotton and soybean and recording injury for 14 d after treatment (DAT) using a rating scale designed to quantify auxin-like herbicide injury. Injury from herbicide volatility was then produced on additional cotton and soybean plants through exposure to vapors of the dimethylamine salt of 2,4-D, diglycolamine salt of dicamba, and butoxyethyl ester of triclopyr using air chambers inside a greenhouse and volatility plots in the field. Injury resulting from this exposure was evaluated for 14 d using the same injury-evaluation scale that was used to produce the rate–response curves. Volatility-injury data were then applied to the rate–response curves so that herbicide rates corresponding with observed injury could be calculated. Using this method, herbicide volatility rates estimated from greenhouse-cotton injury were determined to be 3.0 × 10−3, 1.0 × 10−3, and 4.9 × 10−2 times the use rates of 2,4-D, dicamba, and triclopyr, respectively. Greenhouse-grown soybean developed injury consistent with 1.4 × 10−2, 1.0 × 10−3, and 2.5 × 10−2 times the normal use rate of 2,4-D, dicamba, and triclopyr, respectively. Under field conditions, cotton developed injury symptoms that were consistent with 4.0 × 10−3, 2.0 × 10−3, and 1.25 × 10−1 times the recommended use rates of 2,4-D, dicamba, and triclopyr, respectively. Field soybean displayed injury symptomology concordant with 1.6 × 10−1, 1.0 × 10−2, and 1.1 × 10−1 times the normal use rates of 2,4-D, dicamba, and triclopyr, respectively. This procedure provided herbicide volatility rate estimates that were consistent with rates and injury from the rate–response injury curves. Additional research is needed to ascertain its usefulness in determining long-term effects of drift injury on crop variables such as yield.

Type
Education/Extension
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Arle, H. F. 1954. The sensitivity of Acala 44 cotton to 2,4-D. West. Weed Control Conf. Proc 14:2025.Google Scholar
Behrens, R. and Lueschen, W. E. 1979. Dicamba volatility. Weed Sci. 27:486493.Google Scholar
Bovey, R. W. and Meyer, R. E. 1981. Effects of 2,4,5-T, triclopyr, and 3,6-dichloropicolinic acid on crop seedlings. Weed Sci. 29:256261.Google Scholar
Council for Agricultural Science and Technology. 1975. The Phenoxy Herbicides. Ames, IA: Council for Agricultural Science and Technology Rep. 39. 22 p.Google Scholar
Miller, J. H., Kempen, H. M., Wilderson, J. A., and Fox, C. L. 1963. Response of Cotton to 2,4-D and Related Phenoxy Herbicides. USDA Technical Bulletin 1289.Google Scholar
[SAS] Statistical Analysis Systems. 1985. SAS User's Guide: Statistics. 5th ed. Cary, NC: Statistical Analysis Systems Institute. p. 586.Google Scholar
Sciumbato, A. S., Chandler, J. M., Senseman, S. A., Bovey, R. W., and Smith, K. L. 2004. Determining exposure to auxin-like herbicides. I. Quantifying injury to cotton and soybean. Weed Technol. 18:11251134.CrossRefGoogle Scholar
Seefeldt, S. S., Jensen, J. E., and Fuerst, E. P. 1995. Log-logistic analysis of herbicide dose response relationships. Weed Technol. 9:218227.CrossRefGoogle Scholar
Taylor, A. W. and Spencer, W. F. 1990. Pesticides in the Soil Environment: Processes, Impacts, and Modeling. Madison, WI: Soil Science Society of America. Pp. 213255.Google Scholar
Texas Agriculture Code. 1984. St. Paul, MN: West. Chapter 75.Google Scholar